Kai Yuanqing Xiao: h-index, Total Citations, and Citation Map
Kai Yuanqing Xiao's h-index is 11 (11 i10-index, 33,966+ total citations across 18+ publications) according to Google Scholar as of June 2026. Kai Yuanqing Xiao is affiliated with PhD Student, MIT CSAIL.
Kai Yuanqing Xiao is a researcher affiliated with PhD Student, MIT CSAIL, specializing in Computer Science, Theoretical Machine Learning, Games. Their work has been cited 33,966 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Kai Yuanqing Xiao's Citation Metrics
Bibliometric impact based on 18 indexed publications.
- H-Index
- 11
- i10-Index
- 11
- Total Citations
- 33,966
- Citing Countries
- 40
As of June 2026.
Kai Yuanqing Xiao has an h-index of 11 and 33,966 total citations across 18 publications, with research cited by institutions in 40 countries.
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We've mapped 5,000 of 33,966 citations for Kai Yuanqing Xiao
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Gpt-4 technical report
202327,979
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
17 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher produced a seminal technical report on GPT-4, establishing a foundational reference for large language model capabilities that has garnered nearly 28,000 citations.
The researcher established a rigorous framework for evaluating neural network robustness using mixed integer programming, creating a foundational benchmark for formal verification in deep learning.
The researcher developed a training method to induce ReLU stability, enabling faster adversarial robustness verification in neural networks.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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